An exact approach for the multicommodity network optimization problem with a step cost function

2019 ◽  
Vol 53 (4) ◽  
pp. 1279-1295 ◽  
Author(s):  
Imen Mejri ◽  
Mohamed Haouari ◽  
Safa Bhar Layeb ◽  
Farah Zeghal Mansour
Keyword(s):  
Cost Function ◽  
Network Optimization ◽  
Optimization Problem ◽  
Planning Problem ◽  
Large Set ◽  
Constraint Generation ◽  
Exact Separation ◽  
Installation Cost ◽  
Facility Capacity

We investigate the Multicommodity Network Optimization Problem with a Step Cost Function (MNOP-SCF) where the available facilities to be installed on the edges have discrete step-increasing cost and capacity functions. This strategic long-term planning problem requires installing at most one facility capacity on each edge so that all the demands are routed and the total installation cost is minimized. We describe a path-based formulation that we solve exactly using an enhanced constraint generation based procedure combined with columns and new cuts generation algorithms. The main contribution of this work is the development of a new exact separation model that identifies the most violated bipartition inequalities coupled with a knapsack-based problem that derives additional cuts. To assess the performance of the proposed approach, we conducted computational experiments on a large set of randomly generated instances. The results show that it delivers optimal solutions for large instances with up to 100 nodes, 600 edges, and 4950 commodities while in the literature, the best developed approaches are limited to instances with 50 nodes, 100 edges, and 1225 commodities.

A metaheuristic algorithm for project selection and scheduling with due windows and limited inventory capacity

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1483-1499 ◽  
Author(s):  
Christopher Garcia
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Greedy Heuristic ◽  
Planning Problem ◽  
Large Set ◽  
Effective Solution ◽  
Heavy Industry ◽  
Content Type ◽  
Wide Range

Purpose – The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem. Design/methodology/approach – The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required. Findings – The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions. Practical implications – The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement. Originality/value – This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

scholarly journals EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Yi Ren ◽  
Alparslan Emrah Bayrak ◽  
Panos Y. Papalambros
Keyword(s):  
Optimization Problem ◽  
Solution Space ◽  
Efficient Global Optimization ◽  
Vehicle Design ◽  
Control Problems ◽  
Short Term ◽  
Algorithm Efficiency ◽  
Np Complete ◽  
And Control

We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition and received 2391 game plays by 124 anonymous players during the first month from launch. We found that while only a small portion of human players can outperform the algorithm in the long term, players tend to formulate good heuristics early on that can be used to constrain the solution space. Such constraining of the search enhances algorithm efficiency, even for different game settings. These findings indicate that human-assisted computational searches are promising in solving comprehensible yet computationally hard optimal design and control problems, when human players can outperform the algorithm in a short term.

scholarly journals Trajectory optimization of UAV based on Hp-adaptive Radau pseudospectral method

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yi Cui ◽  
Xintong Fang ◽  
Gaoqi Liu ◽  
Bin Li
Keyword(s):  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Tracking Error ◽  
Pseudospectral Method ◽  
Planning Problem ◽  
A Algorithm ◽  
Planning Scheme ◽  
Path Search ◽  
Simulation Results

<p style='text-indent:20px;'>Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.</p>

Prediction Reliability of Container Terminal Simulation Models

2010 ◽  
pp. 315-335
Author(s):  
Armando Cartenì
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Optimization Problem ◽  
Container Terminal ◽  
Simulation Models ◽  
Large Set ◽  
Model Application ◽  
Before And After ◽  
Prediction Reliability

In this chapter attention is focused on the container terminal optimization problem, given that today most international cargo is transported through seaports and on containerized vessels. In this context, in order to manage a container terminal it is sometimes necessary to develop a Decision Support System (DSS). This chapter investigated the prediction reliability of container terminal simulation models (DSS), through a before and after analysis, taking advantage of some significant investment made by the Salerno Container Terminal (Italy) between 2003 and 2008. In particular, disaggregate and an aggregate simulation models implemented in 2003 were validated with a large set of data acquired in 2008 after some structural and functional terminal modifications. Through this analysis it was possible to study both the mathematical details required for model application and the field of application (prediction reliability) of the different simulation approaches implemented.

scholarly journals A trajectory design method for RLV via artificialmemory-principle optimization

2018 ◽  
Vol 189 ◽  
pp. 10019
Author(s):  
Hao Li ◽  
Changzhu Wei
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Memory Cell ◽  
Optimization Method ◽  
Alternative Solution ◽  
Trajectory Design ◽  
Memory State ◽  
Artificial Memory

A trajectory optimization method for RLV based on artificial memory principles is proposed. Firstly the optimization problem is modelled in Euclidean space. Then in order to solve the complicated optimization problem of RLV in entry phase, Artificial-memory-principle optimization (AMPO) is introduced. AMPO is inspired by memory principles, in which a memory cell consists the whole information of an alternative solution. The information includes solution state and memory state. The former is an evolutional alternative solution, the latter indicates the state type of memory cell: temporary, short-and long-term. In the evolution of optimization, AMPO makes a various search (stimulus) to ensure adaptability, if the stimulus is good, memory state will turn temporary to short-term, even long-term, otherwise it not. Finally, simulation of different methods is carried out respectively. Results show that the method based on AMPO has better performance and high convergence speed when solving complicated optimization problems of RLV.

Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO

2019 ◽  
Vol 07 (02) ◽  
pp. 65-81 ◽  
Author(s):  
Ahmed T. Hafez ◽  
Mohamed A. Kamel
Keyword(s):  
Trajectory Planning ◽  
Optimization Problem ◽  
Fuzzy Logic Controller ◽  
Hybrid Approach ◽  
Task Assignment ◽  
Time Discretization ◽  
Planning Problem ◽  
Dynamic Constraints ◽  
Ground Station ◽  
Novel Approach

This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

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